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(Almost) Hands-Off Information Integration for the Life Sciences Ulf Leser, Felix Naumann Humboldt-Universität zu Berlin Aladin • Basic idea - Urgent need for data integration in the life sciences Life science databases have certain characteristics Life science database users have certain intentions These can be exploited to automate integration • ALmost Automatic Data INtegration for the Life Sciences - Minimize manual effort - Keep quality of integrated data as high as possible - Use domain-specific heuristics Leser. Naumann, Hands-Off Information Integration, CIDR 05 2 Integration? • Database integration • Schema level • Data integration • Data level Leser. Naumann, Hands-Off Information Integration, CIDR 05 Export schema Export schema Federated schema Federated schema Export schema Export schema Export schema Component schema Component schema Component schema Local schema Local schema Local schema Data Source Data Source Data Source 3 Two Cultures of Integration • Schema-driven (computer scientists) - Much smaller than data, (hopefully) well-defined elements Resolve redundancy and heterogeneity at the schema level High degree of automation once system is set-up Focus on methods - you rarely publish a “data paper” • Data-driven (biologists) - Value is in the data, abstraction is a result of analysis - Don‘t bother with schemas • Abstraction is volatile and depends on experimental technique - Manual integration at data level, constant high effort - You rarely publish a (database) “method paper” Leser. Naumann, Hands-Off Information Integration, CIDR 05 4 Two Cultures: TAMBIS & SWISS-PROT • Semantic middleware • 6 sources, 1200 concepts • Ever adopted in any other project? - Integrated schema difficult to understand - No agreement on “global” concepts - Data provenance • Database of protein sequences • Papers, pers. comm., ext. databases, … • Large effort: 30+ data curators - Gold standard database • Mostly perceived and used as a book Leser. Naumann, Hands-Off Information Integration, CIDR 05 5 Linking Associated Objects • Schema-driven - Too abstract; tends to blur data provenance • Data-driven - Costly and time-consuming; inadequate use of DB technology • Alternative: Concentrate on object links • Example: SRS - Maps a flat-file into a semi-structured, “one class” representation - Never mixes data from diff. sources - Use cross-references for navigation and joins Leser. Naumann, Hands-Off Information Integration, CIDR 05 6 Cross-References Leser. Naumann, Hands-Off Information Integration, CIDR 05 7 Aladin’s Scenario • Assumptions - Integration of many, many biological databases - As little manual interventions as possible - Do not merge data from different databases • Challenges - Push automation as far as possible without lowering quality of integrated data too much - Systematically evaluate quality of automatic integration • Why will it work? - Integrate by generating / finding links between objects - Exploit characteristics of life science databases Leser. Naumann, Hands-Off Information Integration, CIDR 05 8 Properties – and how to use them • Data sources have only one “type” of object • Objects have nested, semi-structured annotations Detect hierarchical structure • Objects have stable, unique accession numbers • Databases heavily cross-reference each other Detect objects Detect existing cross-references • Objects have rich annotations (often free text, sequences) Detect further associations based on “similarity” Leser. Naumann, Hands-Off Information Integration, CIDR 05 9 A Biological Database Leser. Naumann, Hands-Off Information Integration, CIDR 05 10 Columba: Multidimensional Integration • • • • Interdisciplinary project Integrates 15 sources annotating protein structures Sources are dimensions for PDB entries Neither data nor schema integration - links SCOP Class Fold Superfamily CATH Class Architecture Topology Homolog. sf DSSP Secondary structure elements SwissProt Description Domains Feature PDB PDB_ID Compounds Chains Ligands GeneOntology Terms TermRelations Ontologies • Advantages • Users recognize their sources • Intuitive query concept • “Relatively” easy to maintain/extend KEGG Pathway Enzyme EC Number Leser. Naumann, Hands-Off Information Integration, CIDR 05 11 Columba Experiences • = Aladin’s assumptions Relational approach feasible: Sources are downloadable, parsers exist Databases are collections of each one type Hierarchical structure, only 1:n relationships Objects have unique accession numbers Importance of and lack of cross references • Lessons learned - Schema reengineering is extremely time-consuming • Although we will only use a small part at the end - There is more demand than resources • Why not be less specific about which data to integrate, but much faster? Leser. Naumann, Hands-Off Information Integration, CIDR 05 12 Materialized Integration Data Warehouse BIND Brenda PDB OMIM Genbank SWISSPROT Leser. Naumann, Hands-Off Information Integration, CIDR 05 PubMed KEGG 13 Materialized Integration BIND Aladin Brenda PDB OMIM Genbank SWISSPROT Leser. Naumann, Hands-Off Information Integration, CIDR 05 PubMed KEGG 14 Five Steps to Integration Source-specific 1. Download source, parse, import into RDBMS 2. Guess primary objects 3. Guess (hierarchically structured) annotation Across data sources 4. Guess cross-references • Objects sharing some piece of information 5. Guess duplicates • Highly similar objects Leser. Naumann, Hands-Off Information Integration, CIDR 05 15 Overview – Steps 1-3 Steps 2 and 3 • Guess primary objects • Guess accession number • Guess / find FK constraints Step 1 • Parse and import • Arbitrary target schema • With or without FK constraints Leser. Naumann, Hands-Off Information Integration, CIDR 05 16 Overview – Steps 4+5 Step 5 • Guess duplicates • Different degrees of “duplicateness” Step 4 • Guess existing cross-refs • Compute new cross-refs Leser. Naumann, Hands-Off Information Integration, CIDR 05 17 1. Download, parse, import • Q: Is that possible in an automatic way? • Q: What is the target schema? • Answers - Here, some manual work is involved, but … Parsers are almost always available (BioXXX) Aladin doesn‘t mind the target schema Target schemas are completely source-specific … may or may not contain FK constraints (MySql is …!) But: Universal relation won’t work Leser. Naumann, Hands-Off Information Integration, CIDR 05 18 2. Guess Primary Objects • Q: What’s a primary object? • Q: How do you find them? • Answers - A database is a collection of objects of one type • Many biological databases started as books - These primary objects have stable accession numbers - Accession numbers look very much the same • P0496, DXS231, 1DXX, … • Analyze length, composition, variation, uniqueness, NOT NULL - But: Databases may have more than one primary type Leser. Naumann, Hands-Off Information Integration, CIDR 05 19 3. Guess Dependent Annotation • Q: Can we detect dependency from data? • Q: What about complex relationships? • Answers - Hierarchical annotation means 1:1 or 1:n relationships • Annotations don’t reference each other • No m:n - especially flat-file parsers don’t generate m:n - Guess or use primary keys and foreign key constraints • Unique and not null; subset relationship; surrogate keys; … - Lot of previous work, e.g. [KL92], [MLP02], … Leser. Naumann, Hands-Off Information Integration, CIDR 05 20 4. Guess Associations between Objects • Q: How can we find existing cross-refs? • Q: How can we generate new cross-refs? • Answers - An existing cross-reference is essentially a pair of identical accession numbers in two different data sources • Same characteristics as accession number (minus uniqueness) - Guess new cross-refs based on similarity of attribute values • Similarity of text fields (text mining), sequences, … - Note: cross-refs are on the object level – need to be stored - Lot of previous work, e.g. [NHT+02], [HBP+05], [AMS+97] Leser. Naumann, Hands-Off Information Integration, CIDR 05 21 5. Guess Duplicates • Q: If we don’t even know classes – what’s a duplicate? • Answer - Most difficult part, but there are many kind-of duplicates • Are sequence-identical genes in different species the same? - Need for varying degrees of “duplicateness” • Data level (overlap in attribute values) • Schema-level (schema matching) - Note: No removal or merging of duplicates - Lot of previous work, e.g. [MGR+02], [BN05], [MLF04], … Leser. Naumann, Hands-Off Information Integration, CIDR 05 22 Caveats • Not meant for high-throughput data - Proteomics profiling, gene expression databases - Targets “knowledge-rich” databases • Resulting warehouse will contain errors - Wrong cross-refs, misinterpreted structure, missing links - Requirement: Measure quality of Aladin’s methods • Use existing integrated databases as gold standard • Precision/recall measures can be derived for all steps • Intended for human usage, not for automatic further processing Leser. Naumann, Hands-Off Information Integration, CIDR 05 23 Summary • Five step (almost) automatic integration procedure - Depends on domain characteristics - Guesses primary objects, annotations, cross-references, duplicates - Neither schema integration nor data fusion – links • Which quality does Aladin achieve? - We don’t know yet – needs to be evaluated • Issue: Scalability - Needs many, many comparisons of tables, tuples, values - But: Incremental integration, sampling, pruning • Issue: Searching and result presentation - Full text search, browsing - But: Queries across sources possible for advanced users Leser. Naumann, Hands-Off Information Integration, CIDR 05 24 Acknowledgements Columba • Humboldt University Silke Trissl Heiko Müller Raphael Bauer • Charite Kristian Rother Stefan Günther Robert Preissner Cornelius Frömmel Leser. Naumann, Hands-Off Information Integration, CIDR 05 • Conrad-Zuse Center Rene Heek Thomas Steinke • Technische Fachhochschule Patrick May Ina Koch • Funding: BMBF 25